HSR: L 1/2-regularized sparse representation for fast face recognition using hierarchical feature selection

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• 作者：Bo Han ; Bo He ; Tingting Sun ; Tianhong Yan ; Mengmeng Ma…
• 关键词：Fast face recognition ; Hierarchical feature selection ; Gabor wavelets ; ELM ; AE ; Sparse representation ; L 1/2 regularization ; HSR
• 刊名：Neural Computing & Applications
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：27
• 期：2
• 页码：305-320
• 全文大小：2,211 KB
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• 作者单位：Bo Han (1)
  Bo He (1)
  Tingting Sun (1)
  Tianhong Yan (2)
  Mengmeng Ma (1)
  Yue Shen (1)
  Amaury Lendasse (3) (4)
  
  1. School of Information Science and Engineering, Ocean University of China, Qingdao, 266000, Shandong, China
  2. School of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou, 310018, Zhejiang, China
  3. Department of Mechanical and Industrial Engineering and the Iowa Informatics Initiative, The University of Iowa, Iowa City, IA, 52242-1527, USA
  4. Arcada University of Applied Sciences, 00550, Helsinki, Finland
  
• 刊物类别：Computer Science
• 刊物主题：Simulation and Modeling
  
• 出版者：Springer London
• ISSN：1433-3058

文摘

In this paper, we propose a novel method for fast face recognition called L _1/2-regularized sparse representation using hierarchical feature selection. By employing hierarchical feature selection, we can compress the scale and dimension of global dictionary, which directly contributes to the decrease of computational cost in sparse representation that our approach is strongly rooted in. It consists of Gabor wavelets and extreme learning machine auto-encoder (ELM-AE) hierarchically. For Gabor wavelets’ part, local features can be extracted at multiple scales and orientations to form Gabor-feature-based image, which in turn improves the recognition rate. Besides, in the presence of occluded face image, the scale of Gabor-feature-based global dictionary can be compressed accordingly because redundancies exist in Gabor-feature-based occlusion dictionary. For ELM-AE part, the dimension of Gabor-feature-based global dictionary can be compressed because high-dimensional face images can be rapidly represented by low-dimensional feature. By introducing L _1/2 regularization, our approach can produce sparser and more robust representation compared to L ₁-regularized sparse representation-based classification (SRC), which also contributes to the decrease of the computational cost in sparse representation. In comparison with related work such as SRC and Gabor-feature-based SRC, experimental results on a variety of face databases demonstrate the great advantage of our method for computational cost. Moreover, we also achieve approximate or even better recognition rate. Keywords Fast face recognition Hierarchical feature selection Gabor wavelets ELM-AE Sparse representation L _1/2 regularization HSR